Identification of Equivalent Stiffness of Bearings in Rotor Bearing Systems Based on GA-BP Neural Network
Aiming at the problem that the equivalent stiffness of bearings is difficult to determine in the me-chanical model of rotor-bearing system,a method for identifying bearing parameters of rotor system based on GA-BP proxy model is proposed.Firstly,the finite element model of the rotor-bearing system is estab-lished and the static and dynamic verification of the model is carried out;Secondly,a test bench is built for modal hammering test to obtain the natural frequency of the first four bending orders;Finally,based on the rotor system simulation model,the GA-BP neural network surrogate model is generated to identify the e-quivalent stiffness of the bearing and do error analysis.The results show that the equivalent stiffness of the bearing can be effectively identified by this method,and the recognition effect of the GA-BP neural network is better than that of the traditional BP neural network,and its maximum error is 1.52%,which proves the feasibility of the proposed method.